def default_xop(xop_type: str, xgraph: XGraph, node: Node,
quant_config: NndctQuantInfo) -> NoReturn:
input_ops: Dict[str, List["xir.Op"]] = {}
if node.has_bound_params():
for param_name, param_tensor in node.op.params.items():
param = xgraph.get_op_by_name(param_tensor.name)
input_ops[param_name.name.lower()] = [param]
input_list = []
for input in node.in_tensors:
if node.has_bound_params() and input.is_param_tensor():
continue
elif input.is_param_tensor():
input_op = xgraph.get_op_by_name(input.name)
else:
input_op = xgraph.get_op_by_name(input.node.name)
input_list.append(input_op)
input_ops["input"] = xgraph.create_input_fix_ops(input_list, node.name,
quant_config)
attrs = _get_xir_attr_from_node(node)
xgraph.create_fixed_normal_op(node.name,
xop_type,
quant_config,
attrs=attrs,
input_ops=input_ops)
def conv_transpose_3d(xgraph: XGraph, node: Node,
quant_config: NndctQuantInfo) -> NoReturn:
attrs = _get_xir_attr_from_node(node)
attrs['kernel'] = attrs['kernel'][::-1]
attrs['stride'] = attrs['stride'][::-1]
attrs['dilation'] = attrs['dilation'][::-1]
attrs['pad'] = list(
itertools.chain.from_iterable([[pad] * 2
for pad in attrs['pad'][::-1]]))
print(attrs)
input_ops: Dict[str, List["xir.Op"]] = {}
if node.has_bound_params():
for param_name, param_tensor in node.op.params.items():
param = xgraph.get_op_by_name(param_tensor.name)
input_ops[param_name.name.lower()] = [param]
input_list = []
for input in node.in_tensors:
if node.has_bound_params() and input.is_param_tensor():
continue
elif input.is_param_tensor():
input_op = xgraph.get_op_by_name(input.name)
else:
input_op = xgraph.get_op_by_name(input.node.name)
input_list.append(input_op)
input_ops["input"] = xgraph.create_input_fix_ops(input_list, node.name,
quant_config)
xgraph.create_fixed_normal_op(node.name,
"transposed_conv3d",
quant_config,
attrs=attrs,
input_ops=input_ops)
def _get_init_param_str(self, node: Node) -> List[str]:
str_list = []
if node.has_bound_params():
return str_list
for param_type, param_tensor in node.op.params.items():
if param_tensor.name not in self._tensor_output_map:
param_name = param_type.value
param_shape = tuple(param_tensor.shape)
param_init_str = f"self.{param_name} = torch.nn.parameter.Parameter(torch.Tensor{param_shape})"
str_list.append(param_init_str)
self._tensor_output_map[
param_tensor.name] = f"self.{param_name}"
return str_list
